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Agent skill

Appfolio Performance Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize AppFolio API performance with caching and batch operations.

MITAuto-check passedBackend & APIs

Install Appfolio Performance Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill appfolio-performance-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace appfolio-performance-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/appfolio-performance-tuning .claude/skills/appfolio-performance-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
appfolio-performance-tuning
GitHub stars
2.8k
Token cost
~1.5k tokens
SKILL.md length
457 words
Files
1
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize AppFolio API performance with caching and batch operations.

  • Works in 4 steps: Start with the smallest safe read and… → Cache only minimized data under a… → Respect the smallest endpoint limit,… → …
  • Tasks that involve Caching
  • SKILL.md covers Overview, Prerequisites, Instructions and Caching Strategy, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Appfolio Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize AppFolio API performance with caching and batch operations. Trigger: "appfolio performance".

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Claude Code

It sits in Backend & APIs, covering Caching. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Tasks that involve Caching

Example prompts

  • “appfolio performance”
  • “/appfolio-performance-tuning”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Bash(curl:*), Grep

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Start with the smallest safe read and capture a baseline before changing
  2. Cache only minimized data under a bounded entry/byte policy and invalidate
  3. Respect the smallest endpoint limit, preserve cursors, and stop parallel
  4. Promote only when performance improves without changing result completeness,

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(npm:*)
    • Bash(curl:*)
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • appfolio.com
    • engineering.appfolio.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Appfolio Performance Tuning loads about 1.5k tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 457 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 457 words, ~1,498 tokens.

Download SKILL.mdSave it as .claude/skills/appfolio-performance-tuning/SKILL.md (or your agent's skills folder).
name
appfolio-performance-tuning
description
Optimize AppFolio API performance with caching and batch operations. Trigger: "appfolio performance".
allowed-tools
Read, Write, Edit, Bash(npm:*), Bash(curl:*), Grep
compatibility
Designed for Claude Code
version
1.5.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, property-management, appfolio, real-estate

AppFolio Performance Tuning

Overview

AppFolio's property management API handles bulk tenant queries, property portfolio pagination, and work order batch processing. Large portfolios with thousands of units generate heavy read traffic on listing endpoints. Optimizing cache lifetimes for slow-changing property data, batching work order updates, and pooling HTTP connections reduces API call volume by 60-80% and cuts dashboard load times from seconds to sub-second.

Prerequisites

  • A measured baseline for latency, call volume, error rate, payload size, cache hit behavior, and data freshness for the specific permitted endpoint.
  • Endpoint-specific rate/concurrency limits, a request budget, and a data policy that excludes tenant, payment, and raw response payloads from generic caches.
  • Synthetic fixtures and a rollback feature flag for validating performance changes without altering production read/write semantics.

Instructions

  1. Start with the smallest safe read and capture a baseline before changing caching, concurrency, pagination, or connection settings.
  2. Cache only minimized data under a bounded entry/byte policy and invalidate on known writes; show stale age to callers where decisions need freshness.
  3. Respect the smallest endpoint limit, preserve cursors, and stop parallel batches before they turn a rate-limit signal into a retry storm.
  4. Promote only when performance improves without changing result completeness, authorization, PII boundaries, or write/idempotency behavior; roll back on any correctness regression.

Caching Strategy

typescript
const cache = new Map<string, { data: unknown; expiry: number }>();
const MAX_CACHE_ENTRIES = 1_000;
const TTL = { properties: 300_000, tenants: 120_000, units: 300_000, workOrders: 60_000 };

async function cached(key: string, ttlKey: keyof typeof TTL, fn: () => Promise<any>) {
  const entry = cache.get(key);
  if (entry && entry.expiry > Date.now()) return entry.data;
  const data = await fn();
  if (!cache.has(key) && cache.size >= MAX_CACHE_ENTRIES) cache.delete(cache.keys().next().value!);
  cache.set(key, { data, expiry: Date.now() + TTL[ttlKey] });
  return data;
}

Batch Operations

typescript
async function batchWorkOrders(client: any, ids: string[], batchSize = 25) {
  const results = [];
  for (let i = 0; i < ids.length; i += batchSize) {
    const batch = ids.slice(i, i + batchSize);
    const res = await Promise.all(batch.map(id => client.http.get(`/work_orders/${id}`)));
    results.push(...res.map(r => r.data));
    if (i + batchSize < ids.length) await new Promise(r => setTimeout(r, 200));
  }
  return results;
}

Connection Pooling

typescript
import { Agent } from 'https';
const agent = new Agent({ keepAlive: true, maxSockets: 10, maxFreeSockets: 5, timeout: 30_000 });
// Pass to axios/fetch: { httpsAgent: agent }

Rate Limit Management

typescript
async function withRateLimit(fn: () => Promise<any>): Promise<any> {
  const res = await fn();
  const remaining = parseInt(res.headers['x-ratelimit-remaining'] || '100');
  if (remaining < 5) {
    const retryAfter = parseInt(res.headers['retry-after'] || '2') * 1000;
    await new Promise(r => setTimeout(r, retryAfter));
  }
  return res;
}

Monitoring

typescript
const metrics = { apiCalls: 0, cacheHits: 0, errors: 0, totalLatency: 0 };
function track(startMs: number, hit: boolean, error?: boolean) {
  metrics.apiCalls++; metrics.totalLatency += Date.now() - startMs;
  if (hit) metrics.cacheHits++; if (error) metrics.errors++;
}
// Log: avg latency, cache hit rate, error rate per minute

Performance Checklist

  • Cache property and unit listings with 5-min TTL
  • Use incremental sync via last_modified timestamps
  • Batch work order updates in groups of 25
  • Enable HTTP keep-alive with connection pooling
  • Parse rate limit headers and back off proactively
  • Parallelize independent dashboard queries with Promise.all
  • Monitor cache hit ratio (target > 70%)
  • Set request timeouts to 30s to avoid hung connections
Show full SKILL.md (177 more words)Show less

Error Handling

IssueCauseFix
429 Too Many RequestsExceeded API rate limitParse Retry-After header, exponential backoff
Stale tenant dataCache TTL too longReduce tenant cache to 2 min, add cache-bust on writes
Timeout on portfolio listLarge dataset with no paginationAdd page_size=100 and cursor-based iteration
Connection resetSocket exhaustionEnable keep-alive agent with maxSockets cap

Output

  • A baseline-to-candidate comparison of latency, call volume, cache hit rate, error rate, and result completeness
  • A bounded/minimized cache and endpoint-specific concurrency policy
  • A rollout or rollback decision with a freshness and correctness receipt

Examples

For a property-dashboard regression, benchmark one synthetic portfolio page, then enable a bounded property-summary cache behind a feature flag. Compare p95 latency, request count, cache hits, and returned IDs before and after the change. Confirm that an authorized write invalidates the affected entry and that a full rate-limit response pauses new work. If the candidate yields stale, partial, unauthorized, or differently ordered results, disable the flag and reconcile before trying another optimization.

Resources

Next Steps

See appfolio-reference-architecture.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/.curated/appfolio-performance-tuning of jeremylongshore/tons-of-skills-marketplace.

Open the folder on GitHubat commit cfae287

Compare with similar skills

Appfolio Performance Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Appfolio Performance Tuning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Appfolio Performance Tuning this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.5kAutomated safety check: PassMIT
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FoundatioFoundatioFx/Foundatio2.1k—~3.9kAutomated safety check: PassApache-2.0
Wp Block Themesgambitph/Stackable3513 repos~985Automated safety check: PassGPL-3.0
Wp Performancegambitph/Stackable3513 repos~1.5kAutomated safety check: PassGPL-3.0
Effect Portable Patternsmillionco/expect3.6k—~3.7kAutomated safety check: PassCustom licence

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Categories

Questions about Appfolio Performance Tuning

What does Appfolio Performance Tuning do?

Optimize AppFolio API performance with caching and batch operations. Appfolio Performance Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize AppFolio API performance with caching and batch operations.

When should I use Appfolio Performance Tuning?

Appfolio Performance Tuning fits situations like: tasks that involve Caching.

How do I install Appfolio Performance Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill appfolio-performance-tuning -a claude-code`. Or copy the skill folder (skills/.curated/appfolio-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/appfolio-performance-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Appfolio Performance Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill appfolio-performance-tuning -a codex`. Or copy the skill folder (skills/.curated/appfolio-performance-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/appfolio-performance-tuning in your project. Codex loads it when a task matches its description.

Can I use Appfolio Performance Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill appfolio-performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/appfolio-performance-tuning, .gemini/skills/appfolio-performance-tuning, .github/skills/appfolio-performance-tuning and .opencode/skills/appfolio-performance-tuning in your project.

What does Appfolio Performance Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Appfolio Performance Tuning is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*), Bash(curl:*), Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Appfolio Performance Tuning access the network?

SKILL.md names 2 domains. As links in the text: appfolio.com and engineering.appfolio.com. This is read from the text; nothing was executed.

Is Appfolio Performance Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Appfolio Performance Tuning use?

Appfolio Performance Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Appfolio Performance Tuning use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Appfolio Performance Tuning?

Skills that share tags, products or a category with Appfolio Performance Tuning: Stripe Projects (fossasia/eventyay, 1.7k stars), Foundatio (FoundatioFx/Foundatio, 2.1k stars), Wp Block Themes (gambitph/Stackable, 351 stars) and Wp Performance (gambitph/Stackable, 351 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Appfolio Performance Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.